Research is a part of AiFi's DNA. We are constantly innovating and disrupting the status quo. AiFi is collaborating with Carnegie Mellon University to lead the way in defining what is Autonomous Retail.
Autonomous retail has the potential to change the way people perceive shopping in a similar way e-commerce did. Autonomous stores could offer the convenience of 24/7 operation close to the customer, eliminate friction (e.g. waiting in line to pay), monitor stock in real-time and better understand human shopping behavior. In recent years, several automated retail technologies have been proposed. However accuracy and cost effectiveness of these approaches have been a major bottleneck preventing large scale deployments and their study. This competition aims to bring industry and academia closer together by reducing the barrier of entry for researchers to access data and infrastructure. This will allow the community to design new approaches and compare their performance under similar conditions.
What will you receive:
- A video feed from 12 cameras inside the store.
- 3D positon of all humans inside the store.
- Weight Sensors data from all sensors on the shelves.
- A trigger that someone entered/exited the store.
- Layout of the sensors and cameras.
- Layout of the products in the store.
- Detailed information of the products.
What will you compute:
Upon receiving the trigger of a person exiting the store you will provide a list of products that the person has exited with.
In order to get you started our organizers have gone shopping.
Please follow this repository to get started on how to use the dataset.
Camera Placement file:
Intrinsics and extrinsics - Here
Simple Example:
Video Data - Here (17.1mb)
Dataset (without depth images) - Here (239 mb)
Complete Dataset (with depth images) - Here (2.0 gb)
See details HERE
UPDATED DUE TO COVID-19
More details about the competition can be found HERE
Team 1:
- Authors: Zhang et al.
- Title: An improved multimodal fusion technique for Cashier-Less Stores
- Affiliation: Harbin Institute of Technology, China
Team 2:
- Authors: Mohammadi et al.
- Title: Advanced Video Processing for Efficient data Analytic
- Affiliation: University of Georgia, U.S.
Team 3:
- Authors: Bao et al.
- Title: Multi-Person Shopping (MPS) for Cashier-Less Store
- Affiliation: Carnegie Mellon University, U.S.
Team 4:
- Authors: Ortiz et al.
- Title: Accurately Aggregating Relevant Target User and Time Based Data
- Affiliation: H-E-B, U.S.
Team 5:
- Authors: Zhang et al.
- Title: Location-aware multi-modal sensor fusion for computational efficient autonomous inventory monitoring system
- Affiliation: UC Merced, U.S.
Team 6:
- Authors: Gao et al.
- Title: Autonomous Checkout for Retail Store--Multi-customer Monitoring
- Affiliation: Stanford University, U.S.
Team 7:
- Authors: Ashok et al.
- Title: Uni-Modal Sensing using Embedded WeightSensors for Fully-Autonomous Store Checkout
- Affiliation: Georgia State University, U.S.
Team 8:
- Authors: Asoke et al.
- Title: Tracking Missplaced item in Autonomous Retail Store
- Affiliation: UC Merced, U.S.
@inproceedings{ruiz2019aim3sDemo,
title={Demo Abstract: Autonomous Inventory Monitoring through Multi-Modal Sensing (AIM3S) for Cashier-Less Stores},
author={Ruiz, Carlos and Falcao, Joao and Pan, Shijia and Noh, Hae Young and Zhang, Pei},
booktitle={Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
pages={395--396},
year={2019},
organization={ACM}
}
@inproceedings{ruiz2019aim3s,
title={AIM3S: Autonomous Inventory Monitoring through Multi-Modal Sensing for Cashier-Less Convenience Stores},
author={Ruiz, Carlos and Falcao, Joao and Pan, Shijia and Noh, Hae Young and Zhang, Pei},
booktitle={Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
pages={135--144},
year={2019},
organization={ACM}
}
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